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3Lifetime Retentiontypical lifetime retention curves of non-paying and payersnegligibledrop-offsignificantdrop-off50% on averageKPI : first day drop-off (50% on average)

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4Lifetime Retention model?horizonLife to date operation of the game modeling retention curvesR(t) = 1 – d * t1/αtparameters d and α are found with estimation techniques• The area under the retention curve is the average lifetime• KPI : quality of retention Q = log(area)

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6First day quitters in a mobile gameZOOM in the first day of the lifetime retentionDecomposition of the 21% drop• 3% leave within the first 15 seconds• 4% leave during the next 4 minutes• 14% leave during the remaining 24 hours• A lot of variation between games• Can help designers to understand whyusers leave

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7Playtime Retention• Users with same playtime canhave a very different lifetime,depending on the intensityand the frequency of play• Example : hardcore user10 h / day on average !Lifetime viewPlaytime viewactivity event• Playtime is a random variable, X = total active time of a user• Retention(t) = Pr(X > t ∣ lifetime > 1), probability of playtime greater than tfor users with lifetime > 1

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8Playtime Retention of a F2P gamenon-paying payers• We only consider users with a lifetime > 1day, complementary to first day drop-off• Impossible to read on a linear time scale• Playtime follows approximately a log-normal distributionKPI : median playtime

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11quickstartslowstartachieve potentialPurchasing Frequency (PF)• Trend is known in 5 daysof observation• Potential PF is predictedby a model based on thecurrent known value• Can’t predict wether thepotential will be achieved• When the curve turnssharply, most of the timeit’s because of poorretention of payers= current value

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12Probability of Purchaseprobability of 1st purchasing day = CRKPI : probability of 2nd purchasing day• Spiral of probability of (re)purchase : 30 days dialrepresentation• Each probability point is the % of payers relative tothe previous point• The interval between two points is the median time• The probability to purchase increaseswith each purchase• 1st & 2nd purchases are critical to success

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14Progression• Ideal case: flat histogram (constant acquisitionof users who keep leveling up)• Outsanding bars signal levels where users quitthe most• Main reasons to quit (based on experience) : unpredictable time interval between levels peak of difficulty in the gameplay boredom• Very often the CR reaches 100% for high levels :this is a symptom of efficient monetizationhooksKPI : no outstanding bars in thehistogram of levels